In recent years, mental health specialists have grown increasingly concerned about how developments of the modern age affect well-being. Social media has contributed to stress among adolescents in particular, as the competition for likes and attention can lead to feelings of anxiety and low self-esteem. Digital connections have also reduced the need for in-person social interaction, and the absence of real-world contact has been proven to take its toll on mental health.
The COVID-19 pandemic has had a similar effect, likely making things worse by mandating social distancing and keeping many people home. We are, after all, social animals and we depend on human connection to thrive. Moreover, the pandemic also caused real economic harm, giving many people good reason to worry about their future.
The accumulated effects of these factors must be taken seriously, but the nature of mental health issues means that they are often invisible to outsiders. Society has long sought better ways of handling mental health issues — from tracking down their causes, to identifying the right solutions, and locating people who need help.
It’s all in the data
Through the ability to access and process enormous amounts of information, Big Data can provide valuable insight on how policy, circumstances, and genes affect people’s mental health, and help us to improve people’s well-being.
If we know what to look for, data patterns can reveal interesting truths. For example, various countries have implemented different policies in response to the COVID-19 pandemic. Which of these countries have seen larger increases in the number of social media posts with words like depression in them?
Similar efforts at pattern recognition can also help to inform economic policy. How high can the unemployment rate go before vast numbers of people start asking Google how to deal with stress? Is there a correlation between social media use and downloads of mental health apps?
Which age groups, economic segments, and personality types are most at risk of experiencing mental health issues when socioeconomic conditions change? Which government policies seem to be most effective at keeping people in a state of good mental health? How much should countries invest in physical health vs mental health?
Answers to these questions may well be found in the data and researchers have already started looking. Businesses like Catasys use predictive analytics to identify people who are at risk from mental health issues so that they can receive the care they need. Other companies, in Asia and elsewhere, are likely to follow suit.
Treatment can also be improved by taking a more comprehensive look at the data. Indeed, personalized mental health management is becoming a central benefit of data collection. Treatment professionals can sort through patients’ behavioral data (with their permission) to get a more precise picture of their emotional state. This level of access to information lets specialists better identify the causes of mental issues, allowing for tailored treatment.
Professional help is paramount
With all that has changed in 2020, it is perhaps no wonder that reports of anxiety, depression, and emotional trauma are on the rise.
Big Data allows us to identify the causes and symptoms of mental health issues more effectively. Yet pattern recognition can only get us so far. We still must prioritize prevention, pass the right public policies, and make the effort to help people who are struggling with depression, anxiety or trauma.
Lastly, remember: Big Data can help us understand what to do, but not how to do it. Treatment for mental health issues takes sensitivity and a special kind of expertise, which can only be delivered by people with deep empathy and an understanding of the complexities of the human condition. If you or a loved one are suffering from a mental illness, please seek help from a trained professional.